testing accuracy

yann/modules/resultor.py

@@ -147,14 +147,15 @@ def update_plot (self, verbose = 2):
         """
         print "TBD"                    
 
-    def print_confusion (self, epoch=0, train = None, valid = None, verbose = 2):
+    def print_confusion (self, epoch=0, train = None, valid = None, test = None, verbose = 2):
         """
         This method will print the confusion matrix down in files. 
 
         Args:
             epoch: This is used merely to create a directory for each epoch so that there is a copy.
             train: training confusion matrix as gained by the validate method.
             valid: validation confusion amtrix as gained by the validate method.
+            test: testing confusion matrix as gained by the test method.
             verbose: As usual.
         """
         if verbose >=3:
@@ -173,14 +174,20 @@ def print_confusion (self, epoch=0, train = None, valid = None, verbose = 2):
         if verbose >=3 :
             print ("... Saving down the confusion matrix")
 
-        self._store_confusion_img (confusion = train,
+        if not train is None:
+            self._store_confusion_img (confusion = train,
                               filename = location + '/train_confusion.eps',
                               verbose = 2)
-
-        self._store_confusion_img (confusion = valid,
+        if not valid is None:
+            self._store_confusion_img (confusion = valid,
                               filename = location + '/valid_confusion.eps',
                               verbose = 2)
 
+        if not test is None:
+            self._store_confusion_img (confusion = test,
+                              filename = location + '/test_confusion.eps',
+                              verbose = 2)
+
     def _store_confusion_img (self, confusion, filename, verbose = 2):
         """
         Convert a normalized confusion matrix into an image and save it down.
@@ -190,14 +197,19 @@ def _store_confusion_img (self, confusion, filename, verbose = 2):
             filename: save the image at the location as a file.
             verbose: as usual.
         """
+        corrects = numpy.trace(confusion)       
+        total_samples = numpy.sum(confusion)
+        accuracy = 100 * corrects / float(total_samples)
         if verbose >= 3:
             print ("... Saving the file down")
         confusion = confusion / confusion.sum(axis = 1)[:,None]
         fig = plt.figure(figsize=(4, 4), dpi=1200)
         plt.matshow(confusion)
         for (i, j), z in numpy.ndenumerate(confusion):
-            plt.text(j, i, '{:0.2f}'.format(z), ha='center', va='center', fontsize=10, color = 'm')        
-        plt.title('Confusion matrix')
+            plt.text(j, i, '{:0.2f}'.format(z), ha='center', va='center', fontsize=10, color = 'm') 
+
+        plt.title('Accuracy: ' + str(int(corrects)) + '/' + str(int(total_samples)) + \
+                                                ' = ' + str(round(accuracy,2)) + '%')
         plt.set_cmap('GnBu')
         plt.colorbar()
         plt.ylabel('True labels')

yann/network.py

@@ -3041,6 +3041,11 @@ def test(self, show_progress = True, verbose = 2):
         labels = []
         total_mini_batches =  self.batches2test * self.mini_batches_per_batch[2]
 
+        if self.network_type == 'classifier':
+            test_confusion_matrix = numpy.zeros((self.num_classes_to_classify,
+                                                                self.num_classes_to_classify),
+                                                        dtype = theano.config.floatX)
+
         if show_progress is True:
             bar = progressbar.ProgressBar(maxval=total_mini_batches, \
                   widgets=[progressbar.AnimatedMarker(), \
@@ -3056,6 +3061,8 @@ def test(self, show_progress = True, verbose = 2):
                 predictions = predictions + self.mini_batch_predictions(minibatch).tolist()
                 if self.network_type == 'classifier':
                     posteriors = posteriors + self.mini_batch_posterior(minibatch).tolist()
+                    test_confusion_matrix = test_confusion_matrix + \
+                                                             self.mini_batch_confusion (minibatch)                     
                 if verbose >= 3:
                     print("... testing error after mini batch " + str(batch_counter) + \
                                                               " is " + str(wrong))
@@ -3066,10 +3073,13 @@ def test(self, show_progress = True, verbose = 2):
         if show_progress is True:
             bar.finish()
 
+        self.cooked_resultor.print_confusion (epoch = 'fin',
+                                            test = test_confusion_matrix,
+                                            verbose = verbose)
+
         total_samples = total_mini_batches * self.mini_batch_size
         if self.network_type == 'classifier':
             testing_accuracy = (total_samples - wrong)*100. / total_samples
-
             if verbose >= 2:
                 print(".. Testing accuracy : " + str(testing_accuracy))
         elif self.network_type == 'generator':